Independent and parallel visual processing of mean, variance, and numerosity: Evidence from dual tasks
The visual system can represent multiple objects in a compressed form of ensemble summary statistics (such as object numerosity, mean, and variance of their features). Yet, the relationships between the different types of visual statistics remains relatively unclear. Here, we tested whether two summaries (mean and numerosity – Experiment 1, and mean and variance – Experiment 2) are calculated independently from each other and in parallel, that is, without cost of dividing attention. Our participants performed dual tasks requiring report about two summaries in each trial, and single tasks requiring report about only one of the summaries. Observers were briefly shown sample sets of circles of various sizes. At test, they had to report the number of circles, their mean size, or the variance of sizes using the adjustment method. The relative difference between an adjusted value and a correct answer was used as a measure of precision. We estimated trial-by-trial correlations between the precision of reports in dual task separately for each observer, as well as correlations between averaged errors in reporting summaries in different conditions across all observers. Both analyses showed (1) the absence of correlations between different types of ensemble statistics suggesting their independence, (2) strong auto-correlations of same-type statistics in different tasks (dual vs. single) suggesting good between-test consistency. We also found no decrement (except that related to the order of report explained by memory retrieval) in performance in dual compared to single tasks, which suggests that two statistics of one ensemble can be processed in parallel. In an additional experiment, we found that the precision of variance reports did not change even when mean size and spatial density changed substantially between sample and adjustment sets. This finding also says for independence between the ensemble statistics.